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	<p>
	<span class="heading"><b>seismic-bumps Data Set</b></span>
	<br><span class="normal"><i><font size="4">Download</font></i>: <a href="http://archive.ics.uci.edu/ml/machine-learning-databases/00266/"><font style="BACKGROUND-COLOR: #FFFFAA" size="4">Data Folder</font></a>, <a href="http://archive.ics.uci.edu/ml/datasets/seismic-bumps#"><font style="BACKGROUND-COLOR: #FFFFAA" size="4">Data Set Description</font></a></span></p>

	<p class="normal"><b>Abstract</b>: The data describe the problem of high energy (higher than 10^4 J) seismic bumps forecasting in a coal 
mine. Data come from two of longwalls located in a Polish coal mine.</p>
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     <td> </td>
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	<tbody><tr>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Data Set Characteristics:&nbsp;&nbsp;</b></p></td>
		<td><p class="normal">Multivariate</p></td>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Number of Instances:</b></p></td>
		<td><p class="normal">2584</p></td>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Area:</b></p></td>
		<td><p class="normal">N/A</p></td>
	</tr>

	<tr>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Attribute Characteristics:</b></p></td>
		<td><p class="normal">Real</p></td>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Number of Attributes:</b></p></td>
		<td><p class="normal">19</p></td>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Date Donated</b></p></td>
		<td><p class="normal">2013-04-03</p></td>
	</tr>
	<tr>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Associated Tasks:</b></p></td>
		<td><p class="normal">Classification</p></td>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Missing Values?</b></p></td>
		<td><p class="normal">N/A</p></td>
		<td bgcolor="#DDEEFF"><p class="normal"><b>Number of Web Hits:</b></p></td>
		<td><p class="normal">51165</p></td>
	</tr>
	<!--
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		<td bgcolor="#DDEEFF"><p class="normal"><b>Highest Percentage Achieved:&nbsp;&nbsp;</b></p></td>
		<td><p class="normal">N/A</p></td>
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	-->
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<br>

<p class="small-heading"><b>Source:</b></p>
<p class="normal">Provide the names, email addresses, institutions, and other contact information of the donors and creators of the data set.
<br>
<br>Marek Sikora^{1,2} (<u>marek.sikora <b>'@'</b> polsl.pl</u>), Lukasz Wrobel^{1} (<u>lukasz.wrobel <b>'@'</b> polsl.pl</u>)
<br>(1) Institute of Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland
<br>(2) Institute of Innovative Technologies EMAG, 40-189 Katowice, Poland</p>

<br>

<p class="small-heading"><b>Data Set Information:</b></p>
<p class="normal">Mining activity was and is always connected with the occurrence of dangers which are commonly called 
<br>mining hazards. A special case of such threat is a seismic hazard which frequently occurs in many 
<br>underground mines. Seismic hazard is the hardest detectable and predictable of natural hazards and in 
<br>this respect it is comparable to an earthquake. More and more advanced seismic and seismoacoustic 
<br>monitoring systems allow a better understanding rock mass processes and definition of seismic hazard 
<br>prediction methods. Accuracy of so far created methods is however far from perfect. Complexity of 
<br>seismic processes and big disproportion between the number of low-energy seismic events and the number 
<br>of high-energy phenomena (e.g. &gt; 10^4J) causes the statistical techniques to be insufficient to predict 
<br>seismic hazard. Therefore, it is essential to search for new opportunities of better hazard prediction, 
<br>also using machine learning methods. In seismic hazard assessment data clustering techniques can be 
<br>applied (Lesniak A., Isakow Z.: Space-time clustering of seismic events and hazard assessment in the 
<br>Zabrze-Bielszowice coal mine, Poland. Int. Journal of Rock Mechanics and Mining Sciences, 46(5), 2009, 
<br>918-928), and for prediction of seismic tremors artificial neural networks are used (Kabiesz, J.: Effect 
<br>of the form of data on the quality of mine tremors hazard forecasting using neural networks. 
<br>Geotechnical and Geological Engineering, 24(5), 2005, 1131-1147). In the majority of applications, the 
<br>results obtained by mentioned methods are reported in the form of two states which are interpreted as 
<br>'hazardous' and 'non-hazardous'. Unbalanced distribution of positive ('hazardous state') and negative 
<br>('non-hazardous state') examples is a serious problem in seismic hazard prediction. Currently used 
<br>methods are still insufficient to achieve good sensitivity and specificity of predictions. In the paper 
<br>(Bukowska M.: The probability of rockburst occurrence in the Upper Silesian Coal Basin area dependent on 
<br>natural mining conditions. Journal of Mining Sciences, 42(6), 2006, 570-577) a number of factors having 
<br>an effect on seismic hazard occurrence was proposed, among other factors, the occurrence of tremors with 
<br>energy &gt; 10^4J was listed. The task of seismic prediction can be defined in different ways, but the main 
<br>aim of all seismic hazard assessment methods is to predict (with given precision relating to time and 
<br>date) of increased seismic activity which can cause a rockburst. In the data set each row contains a 
<br>summary statement about seismic activity in the rock mass within one shift (8 hours). If decision 
<br>attribute has the value 1, then in the next shift any seismic bump with an energy higher than 10^4 J was 
<br>registered. That task of hazards prediction bases on the relationship between the energy of recorded 
<br>tremors and seismoacoustic activity with the possibility of rockburst occurrence. Hence, such hazard 
<br>prognosis is not connected with accurate rockburst prediction. Moreover, with the information about the 
<br>possibility of hazardous situation occurrence, an appropriate supervision service can reduce a risk of 
<br>rockburst (e.g. by distressing shooting) or withdraw workers from the threatened area. Good prediction 
<br>of increased seismic activity is therefore a matter of great practical importance.   The presented data 
<br>set is characterized by unbalanced distribution of positive and negative examples. In the data set there 
<br>are only 170 positive examples representing class 1.
<br></p>

<br>

<p class="small-heading"><b>Attribute Information:</b></p>
<p class="normal">Attribute information:
<br>1. seismic: result of shift seismic hazard assessment in the mine working obtained by the seismic 
<br>method (a - lack of hazard, b - low hazard, c - high hazard, d - danger state);
<br>2. seismoacoustic: result of shift seismic hazard assessment in the mine working obtained by the 
<br>seismoacoustic method;
<br>3. shift: information about type of a shift (W - coal-getting, N -preparation shift);
<br>4. genergy: seismic energy recorded within previous shift by the most active geophone (GMax) out of 
<br>geophones monitoring the longwall;
<br>5. gpuls: a number of pulses recorded within previous shift by GMax;
<br>6. gdenergy: a deviation of energy recorded within previous shift by GMax from average energy recorded 
<br>during eight previous shifts;
<br>7. gdpuls: a deviation of a number of pulses recorded within previous shift by GMax from average number 
<br>of pulses recorded during eight previous shifts;
<br>8. ghazard: result of shift seismic hazard assessment in the mine working obtained by the 
<br>seismoacoustic method based on registration coming form GMax only;
<br>9. nbumps: the number of seismic bumps recorded within previous shift;
<br>10. nbumps2: the number of seismic bumps (in energy range [10^2,10^3)) registered within previous shift;
<br>11. nbumps3: the number of seismic bumps (in energy range [10^3,10^4)) registered within previous shift;
<br>12. nbumps4: the number of seismic bumps (in energy range [10^4,10^5)) registered within previous shift;
<br>13. nbumps5: the number of seismic bumps (in energy range [10^5,10^6)) registered within the last shift;
<br>14. nbumps6: the number of seismic bumps (in energy range [10^6,10^7)) registered within previous shift;
<br>15. nbumps7: the number of seismic bumps (in energy range [10^7,10^8)) registered within previous shift;
<br>16. nbumps89: the number of seismic bumps (in energy range [10^8,10^10)) registered within previous shift;
<br>17. energy: total energy of seismic bumps registered within previous shift;
<br>18. maxenergy: the maximum energy of the seismic bumps registered within previous shift;
<br>19. class: the decision attribute - '1' means that high energy seismic bump occurred in the next shift 
<br>('hazardous state'), '0' means that no high energy seismic bumps occurred in the next shift 
<br> ('non-hazardous state').</p>

<br>

<p class="small-heading"><b>Relevant Papers:</b></p>
<p class="normal">N/A</p>

<br>


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<p class="small-heading"><b>Papers That Cite This Data Set<sup>1</sup>:</b></p>
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<br>

<p class="small-heading"><b>Citation Request:</b></p>
<p class="normal">Citation request
<br>Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of data collected by seismic 
<br>hazard monitoring systems in coal mines. Archives of Mining Sciences, 55(1), 2010, 91-114.</p>

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<!-- OLD CODE:
<p class="normal"><font size=1>[1] Papers were automatically harvested and associated with this data set, in collaboration with <a href="http://rexa.info"><font size=1>Rexa.info</font></a></font></p>
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